CONOP 1: Incident Response
The transition from manual triage to autonomous orchestration leverages machine-speed response to bridge critical detection gaps within institutional frameworks.
Problem
Historical reliance on static signatures and manual triage creates vulnerabilities bypassable by polymorphic APTs and machine-speed threats.
Mission & Intent
Deployment of high-confidence autonomous orchestration nodes to reduce the detection-to-containment response gap in institutional SOCs.
Operational Overview
Desired Effects
Establishing a distributed neural fabric for real-time monitoring, transitioning from reactive workflows to pro-active machine learning defense cycles.
Real-time preemption of sophisticated lateral movement and a significant reduction in network exposure during active exploit stages.
End State
A fully integrated AI-driven defense posture that maintains sovereign command while operating at machine-speeds against global threats.
CONOP 2: Insider Threat Monitoring
The core objective of this operational concept is to detect behavioral anomalies that signal internal compromise or malicious intent through persistent orchestration.
Problem
Legacy systems fail to identify credentialed users executing unauthorized lateral movement or low-frequency exfiltration patterns.
Operational Overview
Establishing a distributed neural fabric for real-time monitoring, transitioning from reactive workflows to pro-active machine learning defense cycles.
End State
A fully predictive interior perimeter that responds to high-confidence behavioral outliers with autonomous defensive orchestration.
Mission & Intent
Establish a behavioral-neutral baseline to proactively isolate entity-based threats within the infrastructure through autonomous discovery cycles.
Desired Effects
Real-time isolation of compromised credentials and immediate nullification of exfiltration attempts before lateral movement stages.